Title

The Data-Driven δ-Generalized Labeled Multi-Bernoulli Tracker for Automatic Birth Initialization

Abstract

The δ-generalized labeled multi-Bernoulli (δ-GLMB) tracker is the first multiple hypothesis tracking (MHT)-like tracker that is provably Bayes-optimal. However, in its basic form, the δ-GLMB provides no mechanism for adaptively initializing targets at their first appearance from unlabeled measurements. By introducing a new multitarget likelihood function that accounts for new target appearance, a data-driven δ-GLMB tracker is derived that automatically initializes new targets in the tracker measurement update. Monte Carlo results of simulated multitarget tracking problems demonstrate improved multitarget tracking accuracy over comparable adaptive birth methods.

Meeting Name

Signal Processing, Sensor/Information Fusion, and Target Recognition XXVII 2018 (2018: Apr. 16-19, Orlando, FL)

Department(s)

Mechanical and Aerospace Engineering

Comments

This research was funded by the Laboratory Directed Research and Development (LDRD) Program at Sandia National Laboratories. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Keywords and Phrases

Monte Carlo methods, Bayes-optimal; Likelihood functions; Measurement updates; Monte Carlo results; Multi-Bernoulli; Multi-target tracking; Multiple hypothesis tracking; Multitarget, Signal processing

International Standard Book Number (ISBN)

978-151061803-9

International Standard Serial Number (ISSN)

0277-786X; 1996-756X

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2018 SPIE, All rights reserved.

Share

 
COinS